Volume 22, Issue 2, April 2016, Pages 401–407
K. Vidhya1 and R. Shanmugalakshmi2
1 Department of Computer Science and Engineering, KPR institute of Engineering and Technology, Coimbatore, India
2 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Diabetes Mellitus is one of the growing vitally fatal diseases world-wide. Diabetes causes serious health issues invariantly to all. It will not bother about age, ethnic and also the racial group of a people. Diabetes is a Chronic Disease which is increasing rapidly due to the lack of awareness, change in urban culture, unhealthy foods, lack of physical activity and also due to hereditary. As per the statistical reports the impact of Type2 diabetes is very high comparing to Type 1 diabetes. Especially diabetes on children, adults, pregnant women and also people those who are suffering by other serious diseases needs to be monitored closely and their risks should be addressed specially through various researches and studies. Creating awareness and imparting knowledge about managing diabetes is very essential to safeguard our future world. Due to huge population, the volume, velocity and also the varieties of diabetic data increases tremendously. So for storing such large volume of data we need an elastically scalable environment such as Cloud. Cloud is a fast deployable and scalable platform very much suitable for accommodating huge amount of dynamic data. While handing those large size of data we will meet the problems of data synchronization, concurrency, job scheduling and fault tolerance. By applying the Hadoop-programming model the fore mentioned problems can be handled in a simple and efficient way. The dynamic and large sized health care data can be effectively stored and processed by using the proposed architecture where the risk factor (in terms of %) for type 2 diabetes is extracted based on the ratio of BMI (Body Mass Index) and age. This Cloud based framework helps the patients and physicians to access their data globally anywhere at any time.
Author Keywords: Diabetes, Dynamic data, Data Analysis, BMI, Cloud Storage.
K. Vidhya1 and R. Shanmugalakshmi2
1 Department of Computer Science and Engineering, KPR institute of Engineering and Technology, Coimbatore, India
2 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, India
Original language: English
Copyright © 2016 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Diabetes Mellitus is one of the growing vitally fatal diseases world-wide. Diabetes causes serious health issues invariantly to all. It will not bother about age, ethnic and also the racial group of a people. Diabetes is a Chronic Disease which is increasing rapidly due to the lack of awareness, change in urban culture, unhealthy foods, lack of physical activity and also due to hereditary. As per the statistical reports the impact of Type2 diabetes is very high comparing to Type 1 diabetes. Especially diabetes on children, adults, pregnant women and also people those who are suffering by other serious diseases needs to be monitored closely and their risks should be addressed specially through various researches and studies. Creating awareness and imparting knowledge about managing diabetes is very essential to safeguard our future world. Due to huge population, the volume, velocity and also the varieties of diabetic data increases tremendously. So for storing such large volume of data we need an elastically scalable environment such as Cloud. Cloud is a fast deployable and scalable platform very much suitable for accommodating huge amount of dynamic data. While handing those large size of data we will meet the problems of data synchronization, concurrency, job scheduling and fault tolerance. By applying the Hadoop-programming model the fore mentioned problems can be handled in a simple and efficient way. The dynamic and large sized health care data can be effectively stored and processed by using the proposed architecture where the risk factor (in terms of %) for type 2 diabetes is extracted based on the ratio of BMI (Body Mass Index) and age. This Cloud based framework helps the patients and physicians to access their data globally anywhere at any time.
Author Keywords: Diabetes, Dynamic data, Data Analysis, BMI, Cloud Storage.
How to Cite this Article
K. Vidhya and R. Shanmugalakshmi, “Cloud based framework to handle and analyze diabetes data C,” International Journal of Innovation and Scientific Research, vol. 22, no. 2, pp. 401–407, April 2016.
